This invention relates in general to optical signal processing, including the use of two-dimensional optical delay lines through which transfer rate control of optical data is exercised.
Pattern recognition systems involving identification and classification of input data are presently utilized for a wide variety of purposes including but not limited to product line inspection, computer text scanning, robotics, medical diagnostics, fingerprint identification, product code scanners in supermarkets, airport security and character recognition. There is also significant military interest in pattern recognition for autonomous identification of enemy targets through a smart weapon system where speed and accuracy requirements are high. Such requirements can be met by the high throughput realized from the parallelism inherent in optical signal processing systems.
There are several classes of pattern classification techniques, including feature extractions, correlation filters or template matching, and matched filtering. In the feature extraction technique, feature characteristics of the input data to be classified are a priori extracted from the input data. Those extracted features are then used for feature location in subsequent input data for classification purposes. An inherent problem with this technique is in the selection of the characteristic features, and in potential loss of significant information.
The correlation filter technique is used in pattern classification by finding the closest match between a correlation filter specific to a given class. The closest match identifies the class to which the input data belongs. The latter technique is not very sensitive and cannot discriminate between highly correlated classes.
The match filter is used in a pattern classification technique in a manner similar to that of the correlation filters. The input pattern is compared with a number of stored filters or patterns and the closest match is considered to be the class to which the input data belongs. While the matched filter technique provides the maximum possible signal to noise ratio for pattern detection in white additive noise, its performance will degrade significantly in non-white additive noise.
The foregoing classification techniques are highly sensitive to change of the input data in scale and rotation. A change of 5% in the scale of the image, for example, will reduce detection probability by at least 50% while a change in rotation of 5% has the same effect. Three methods are used to compensate for the scale and rotation invariance problem in pattern recognition. One of the methods is to create smart filters in which the input data is mathematically mapped into a scale and rotation invariant construct. This method is computationaly intensive and suffers from noise and distortion sensitivity. Another method is to use many filters, each filter differing in scale and/or rotation, and matching the input data with all possible variations of rotation and scale. The latter method requires a large amount of filters, and slows the classification rate significantly since the filters have to be mechanically changed or electronically updated through a slow electronic interface. Yet another method involves storing multiple patterns of the same class with variation in scale and rotation on a single filter. Such technique has the disadvantage that as the number of patterns stored on a single filter increases, the performance of the filter degrades rapidly.
A possible solution to the foregoing problems is to use an optical ring. However, with use of the optical ring the transfer rate is uncontrolled since data is transferred within the ring at the speed of light. No existing electronic or optical/electronic hybrid systems or devices can interface at such a high data transfer rate. Another significant problem arises because the optical gain is significantly less than one while the optical intensity decreases rapidly with each iteration within the optical ring until all data is, for all intent and purpose, lost.
The foregoing problems and proposed solutions are not limited to scale and rotation for optical pattern classification but also to any optical processing system implementing any type of iterative process, including but not limited to, optical associative memory, artificial neural networks, and optical wavelet transforms.
It is therefore an important object of the present invention to introduce a two-dimension optical ring with variable transfer rate and unity gain, thus resolving major problems of the optical ring and allow practical implementations of optical iterative processing in any optical signal processing system requiring iterative processing such as, scale and rotation invariant pattern classification and including optical associative memories, optical artificial neural network and optical wavelet processing.